Unsupervised feature selection method for improved human gait recognition

Rida, Imad, Al-Maadeed, Somaya and Bouridane, Ahmed (2015) Unsupervised feature selection method for improved human gait recognition. In: 23rd European Signal Processing Conference (EUSIPCO), 31 August - 4 September 2015, Nice.

Full text not available from this repository.
Official URL: http://dx.doi.org/10.1109/EUSIPCO.2015.7362559


Gait recognition is an emerging biometric technology which aims to identify people purely through the analysis of the way they walk. The technology has attracted interest as a method of identification because it is non-invasiveness since it does not require the subject's cooperation. However, "covariates" which include clothing, carrying conditions, and other intra-class variations affect the recognition performances. This paper proposes an unsupervised feature selection method which is able to select most relevant discriminative features for human recognition to alleviate the impact of covariates so as to improve the recognition performances. The proposed method has been evaluated using CASIA Gait Database (Dataset B) and the experimental results demonstrate that the proposed technique achieves 85.43 % of correct recognition.

Item Type: Conference or Workshop Item (Paper)
Subjects: G400 Computer Science
G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Ellen Cole
Date Deposited: 09 May 2016 11:17
Last Modified: 08 Sep 2020 15:27
URI: http://nrl.northumbria.ac.uk/id/eprint/26765

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics